The LOFAR Central Processing Facility Architecture

Schaaf, K. van der; Broekema, Chris; Diepen, Ger van; Meijeren, Ellen Van

doi:10.1007/1-4020-3798-8_5

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…The total number of baselines, B, in an array of A antennas is B = A(A − 1)/2, and it is a significant performance parameter of the radiotelescope. For example, the LOFAR radiotelescope [26] has a total of B = 1830 baselines, while SKA [22] should have approximately 1500 times as many.…”

Section: Radio Interferometrymentioning

confidence: 99%

“…A large part of current radioastronomy research focuses on building larger radio telescopes with better resolutions. Projects like LOFAR [26], ASKAP [6] or SKA [22] aim to provide highly accurate astronomical measurements by collecting huge streams of radio synthesis data, which are further processed in several stages and transformed into high-resolution sky images. When designing and deploying this data processing chain, radio astronomers have quickly reached the point where computational power and its efficient use is critical.…”

Section: Introductionmentioning

confidence: 99%

“…When designing and deploying this data processing chain, radio astronomers have quickly reached the point where computational power and its efficient use is critical. For example, it is estimated that LOFAR can produce over 100 TB/day [26]; for SKA, which has about 1500 times more antennas, the number will increase with at least 5 orders of magnitude. For such huge collections of radioastronomy data, whatever cannot be processed in time has to be stored, and whatever cannot be stored is lost.…”

Section: Introductionmentioning

confidence: 99%

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Building High-Resolution Sky Images Using the Cell/B.E.

Vărbănescu

Amesfoort

Diepen

et al. 2009

Scientific Programming

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The performance potential of the Cell/B.E., as well as its availability, have attracted a lot of attention from various high-performance computing (HPC) fields. While computation intensive kernels proved to be exceptionally well suited for running on the Cell, irregular data-intensive applications are usually considered as poor matches. In this paper, we present our complete solution for enabling such a data-intensive application to run efficiently on the Cell/B.E. processor. Specifically, we target radioastronomy data gridding and degridding, two resembling imaging filters based on convolutional resampling. Our solution is based on building a high-level application model, used to evaluate parallelization alternatives. Next, we choose the one with the best performance potential, and we gradually exploit this potential by applying platform-specific and application-specific optimizations. After several iterations, our target application shows a speed-up factor between 10 and 20 on a dual-Cell blade when compared with the original application running on a commodity machine. Given these results, and based on our empirical observations, we are able to pinpoint a set of ten guidelines for parallelizing similar applications on the Cell/B.E. Finally, we conclude the Cell/B.E. can provide high performance for data-intensive applications at the price of increased programming efforts and with a significant aid from aggressive application-specific optimizations.

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Section: Radio Interferometrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Building High-Resolution Sky Images Using the Cell/B.E.

Vărbănescu

Amesfoort

Diepen

et al. 2009

Scientific Programming

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“…Each station produces samples from different subbands (frequency ranges), which are complex numbers representing the amplitude and phase of a wave at a particular moment. The data from all stations are centrally collected via a wide-area network, filtered, and correlated in real time on a six-rack Blue Gene/L supercomputer [14,15]. Currently, 16 stations have been partially built, with a bandwidth limited to 500 Mb/s (48 subbands) each.…”

Section: Communicating Real-time Telescope Data With Zoidmentioning

confidence: 99%

Zoid

Iskra

Romein

Yoshii

et al. 2008

Proceedings of the 13th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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The ZeptoOS project is developing an open-source alternative to the proprietary software stacks available on contemporary massively parallel architectures. The aim is to enable computer science research on these architectures, enhance community collaboration, and foster innovation. In this paper, we introduce a component of ZeptoOS called ZOID-an I/O-forwarding infrastructure for architectures such as IBM Blue Gene that decouple file and socket I/O from the compute nodes, shipping those functions to dedicated I/O nodes. Through the use of optimized network protocols and data paths, as well as a multithreaded daemon running on I/O nodes, ZOID provides greater performance than does the stock infrastructure. We present a set of benchmark results that highlight the improvements. Crucially, the flexibility of our infrastructure is a vast improvement over the stock infrastructure, allowing users to forward data using custom-designed application interfaces, through an easy-to-use plug-in mechanism. This capability is used for realtime telescope data transfers, extensively discussed in the paper. Plug-in-specific threads implement prefetching of data obtained over sockets from an input cluster and merge results from individual compute nodes before sending them out, significantly reducing required network bandwidth. This approach allows a ZOID version of the application to handle a larger number of subbands per I/O node, or even to bypass the input cluster altogether, plugging the input from remote receiver stations directly into the I/O nodes. Using the resources more efficiently can result in considerable savings.

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“…For example, radioastronomy projects like LOFAR [2], AS-KAP [3], or SKA [4] provide highly accurate astronomical measurements by collecting huge streams of radio synthesis data, which are further processed into sky images. For radioastronomy applications, processing power and storage space need to be used with extreme efficiency: whatever is not computed in time, may get stored; whatever does not fit in the storage space gets lost.…”

Section: Introductionmentioning

confidence: 99%

Radioastronomy Image Synthesis on the Cell/B.E.

Vărbănescu

Amesfoort

Mattingly

et al. 2008

Lecture Notes in Computer Science

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Abstract. Now that large radiotelescopes like SKA, LOFAR, or AS-KAP, become available in different parts of the world, radioastronomers foresee a vast increase in the amount of data to gather, store and process. To keep the processing time bounded, parallelization and execution on (massively) parallel machines are required for the commonly-used radioastronomy software kernels. In this paper, we analyze data gridding and degridding, a very time-consuming kernel of radioastronomy image synthesis. To tackle its its dynamic behavior, we devise and implement a parallelization strategy for the Cell/B.E. multi-core processor, offering a cost-efficient alternative compared to classical supercomputers. Our experiments show that the application running on one Cell/B.E. is more than 20 times faster than the original application running on a commodity machine. Based on scalability experiments, we estimate the hardware requirements for a realistic radio-telescope. We conclude that our parallelization solution exposes an efficient way to deal with dynamic data-intensive applications on heterogeneous multi-core processors.

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The LOFAR Central Processing Facility Architecture

Cited by 4 publications

References 6 publications

Building High-Resolution Sky Images Using the Cell/B.E.

Building High-Resolution Sky Images Using the Cell/B.E.

Zoid

Radioastronomy Image Synthesis on the Cell/B.E.

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